Lecture 11 Notes, Nonparametric Statistics
Does not depend on the population fitting any particular type of distribution
(e.g, normal). Make fewer assumptions and apply more broadly at the
expense of a less powerful test (needing more observations to draw

Lecture 10 Notes, Regression
Regression analysis allows us to estimate the relationship of a response
variable to a set of predictor variables
Let
x1, x2, xn
be settings of x chosen by the investigator and
y1, y2, yn
be the corresponding values of the res

Lecture 2 Notes, Data
A population is a collection of objects, items, humans/animals (units) about
which information is sought.
A sample is a part of the population that is observed.
A parameter is a numerical characteristic

Lecture 9 Notes, Two-Sample Inference
Independent Samples Design:
There are a few dierent ways we can do an experiment. In an independent samples design,
we have an independent sample from each population. The data from the two groups are independent.
Sa

Lecture 6 Notes, Inference
Statistical Inference is the process of making conclusions using data that is subject to random variation.
Bias() := E() , where is the true parameter value and is an estimate of it
computed from data.
Mean-Squared Error (MSE)

Lecture 8 Notes, Single Sample Inference
You know already for a large sample, you can invoke the CLT so:
2
X N(, ).
Also for a large sample, you can replace an unknown by s.
know how to do a hypothesis test for the mean, either:
calculate z-statistic a